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We develop a high-Q capacitive sensing based robust non-destructive evaluation (NDE) methodology that can be widely used in varied NDE applications. We show that the proposed method can detect defects in a host of robust regimes where uncertainties such as lift-off, probe tilt, fluctuations in sampling rates, and step sizes are inherent in the data collection process. We explicitly characterize the corruption in the capacitive sensing data due to various lift-off based uncertainties. We use a Bayesian decision theoretic approach to rigorously understand the impact of these corruptions on defect identification efficacy. Using an optimally tuned weighted classification loss, we prove that it is theoretically feasible to accurately detect defect location and sizes from capacitive sensing signals collected under the aforementioned uncertainties. The Bayesian decision theoretic study needs prior information for accurate detection that is not available in real NDE inspections. So, we develop a solely data driven algorithm that analyzes the capacitive sensing signals without any prior knowledge of defect or uncertainty types. The developed algorithm is non-parametric and uses spatially adaptive denoising to weed out uncertainty induced noises. By leveraging the spatial association in the capacitive sensing signals, our algorithm greatly improves on popular non-spatial approaches. Compared to popular thresholding methods and low-rank based denoising approaches, we demonstrate superior performance of the proposed method in terms of coverage and false positive metrics for defect identification. Using spatially adaptive denoising, we design a robust capacitive sensing method that can detect defects with high precision under various uncertainty regimes. [ABSTRACT FROM AUTHOR]

Magnetic flux leakage (MFL) pigs are traditionally used for the detection of gross corrosion on steel pipelines used for the transmission of natural gas. Alternative nondestructive evaluation (NDE) modalities are required for the detection of stress corrosion cracking (SCC), which tends to exist in colonies oriented axially along the length of the pipeline. This paper describes the use of multi-phase rotating magnetic fields in the remote region of the probe as a possible SCC detection mechanism. Details of a prototype pig and test rig are given and the challenges associated with the finite element modeling of the device are discussed. Initial experimental results show that this novel NDE modality is sensitive to axially oriented tight cracks. [Copyright &y& Elsevier]

In recent years, nondestructive testing and evaluation techniques have been successfully used to evaluate the integrity on different kinds of materials. These methods have been exploited in a lot of interesting industrial applications, since they have the advantage to keep the inspected specimens intact. In particular, within the framework of ageing aircrafts' inspection, magneto-optic imaging (MOI) is increasing in importance and popularity. Usually, the open problem with MOI is the identification of bad rivets starting from experimental measurements. For this purpose, the Karhunen-Loeve transform, an eigenvector singular value decomposition based technique, has been exploited by authors. In this paper, the implemented method and very interesting preliminary results are described. [ABSTRACT FROM AUTHOR]

Abstract: Periodic assessment of ageing civil structures is important for structural integrity and public safety. Numerous destructive and non-destructive techniques employing different energy sources have been proposed for structural health monitoring. Amongst the non-destructive evaluation techniques proposed for monitoring material strength and contents of concrete structures, techniques employing microwaves offer distinct advantages in that they are non-radioactive and provide good penetration, excellent contrast between steel reinforcement bar and concrete, and insensitivity to ambient temperature. In this paper, the feasibility of a far-field microwave NDE technique for concrete imaging is investigated via numerical simulations and experiments on cement based samples using far-field microwave reflection coefficients. [Copyright &y& Elsevier]

Abstract Nitrogen contamination of weld material affects mechanical properties and results in small change of the electric conductivity. This small difference of resistivity needs to be measured since the material property is affected by the nitrogen contamination level. A major challenge in determining the contamination level is the accurate measurement of resistivity in the presence of lift-off variation, top oxide layer and surface irregularities. In this paper, a novel approach based on Decay Time (DT) using Pulsed Eddy Current (PEC) and a high sensitive magnetoresistive (MR) sensor is proposed to measure small changes in electrical conductivity of materials. The DT parameter defined herewith is linearly correlated with material conductivity allowing it to be used for estimating the conductivity from transient PEC measurements. Moreover, this method is less sensitive to lift-off variations. The operation principle of this method is demonstrated using finite element model (FEM) simulation and validated using a prototype probe comprising an excitation coil and MR sensor. Weld metal samples with varying levels of nitrogen contamination are tested using the prototype probe demonstrating the feasibility of the method. [ABSTRACT FROM AUTHOR]

Abstract: Non-Destructive Evaluation (NDE) methods are used to inspect materials without damaging their usefulness. The key problem in NDE is the inverse problem which involves reconstructing materials’ physical profiles, like inner discontinuity etc., using information in the measured NDE signal. Inverse problem solutions in NDE can be classified as model-based and system-based approach. In model-based approach, an accurate forward model is used in an iterative framework. This approach provides a resultant materials' physical profile that minimizes the error between the measured signal and a simulated signal. However, this approach requires repeated calculations of a numerical model in each iteration, making it computationally demanding. This paper presents a model-based method that also provides a direct approach to inversion using principles of time reversal. Time reversal focusing is based on the fact that when a wave solution is reversed in time and back-propagated the wave comes to focus at the source. Using a computational model, this paper applies principles of time reversal to microwave NDE data to solve the inverse problem of defect detection in dielectric materials. A two-dimensional finite difference time domain (FDTD) model, for simulating the propagation of forward and time reversed wave fields, is developed. A dielectric sample with artificial defects, illuminated by a Gaussian modulated pulse, is used in the simulations. The microwave measurements are recorded, time reversed and propagated using the FDTD model to highlight the scatterer/defect. Maxima in the energy image indicate locations of defects. Simulation results demonstrate the feasibility of the technique to detect defects in dielectric materials. The FDTD model is validated using experimental data. [Copyright &y& Elsevier]

Abstract: In the application of electromagnetic nondestructive evaluation (NDE), the measured signals received by sensors are decided by the flaw associated with operational parameters during inspection. The techniques of invariant pattern recognition have been studied to render NDE signals insensitive to operational variations and preserve or recover crack information. The invariant scheme and algorithms have been addressed to facilitate magnetostatic flux leakage and eddy current NDE, which eliminated operational parameters and lift-off changes corrupt from signal measurements. A novel invariance analysis of eddy current (EC) signals in the inspection of deeply embedded cracks under layered fastener heads has been presented in this paper. A detection system based on uniform EC excitation and giant-magnetoresistive (GMR) pick-up sensors has been developed and shown improved detectability of 2nd and 3rd layer defects around fastener sites in multilayer structures. However, the sensor-tilt due to variation of probe lift-off can generate redundant response as noise effect and obscure the flaw inspection. The variations of GMR sensor-tilt with crack inspection are investigated using an efficient numerical model that simulates the used EC-GMR system. The scheme of invariance transformation is proposed to exclude the sensor-tilt noise and keep the defect inspection identical. The statistical features insensitive to tilt effects are extracted after the invariance processing, which has ensured the probability of flaw detection. [Copyright &y& Elsevier]

Abstract: The problem of detecting deep embedded cracks under fastener heads (CUF) in multilayer structures has resulted in the development of several variations of eddy current based techniques. Eddy current excitation combined with magnetoresistive (MR) sensing has improved the effectiveness in detecting second and third layer defects around fastener heads. An eddy current-giant magnetoresistive (EC-GMR) sensor system has been developed and shown to be feasible in detecting subsurface cracks at fastener sites with aluminum fasteners. The normal component of induced magnetic field is used to identify defective fastener sites by exploiting the asymmetry information in the measured signal. However, the problem of detecting cracks under steel fasteners remains a major challenge since the dominant signals from steel fasteners can mask the contribution from small subsurface defects. This paper presents the feasibility study of detecting cracks under steel fasteners using additional information contained in the tangential components and of induced magnetic field. A model-based study exploiting information in all components using image fusion techniques is presented and feasibility of the approach for automated defect detection is shown. [Copyright &y& Elsevier]

This paper describes the use of two simple electromagnetic methods for detecting strut fractures in prosthetic heart valves. The first method involves immersing the heart valve in a uniform time-varying electromagnetic field and measuring the perturbation of the field in regions proximate to the strut. In vitro tests done to date indicate that the method is capable of discriminating between intact and fractured struts. The second method that is currently being investigated involves the use of electromagnetic-acoustic transduction methods for exciting the resonant modes of the outlet strut. Differences between the frequencies associated with the resonant modes of intact and fractured struts are exploited to diagnose the state of the valve. [ABSTRACT FROM AUTHOR]

Compared with the conventional finite element method that relies on an underlying mesh and element-node connectivity to describe the geometry, meshless method shows its advantage in mesh simplification, tight crack representation and re-meshing process. An effective numerical implementation of the meshless method uses the Galerkin technique and is referred to as the element-free Galerkin (EFG) method. In this paper, the EFG method is applied to a field of electromagnetic computation, namely, simulation of pulsed eddy current testing. The model is validated against analytical solutions. [ABSTRACT FROM AUTHOR]

This paper presents a generalized likelihood ratio technique for detection of defect locations from bobbin coil eddy current data. First a Neyman–Pearson (NP) decision rule for detection of known random signals (in presence of noise) is discussed. The result is then generalized to the problem of detection of unknown random signals that are commonly found in bobbin coil eddy current data. The performance of the proposed detection technique is tested on several real world data sets collected from the steam generator tubes of nuclear power plants. The experimental results indicate that the method is quite promising and useful for automated processing and classification of eddy current data. [Copyright &y& Elsevier]

In this paper, we present two feature extraction techniques for the classification of ultrasonic NDE signals acquired from weld inspection regions of boiling water reactor piping of nuclear power plants. The classification system consists of a pre-processing block that extracts features from the incoming patterns, and of an artificial neural network that assigns the computed features to a particular class of defect present on the inspected pipe. The two techniques are respectively based on t he discrete Gabor transform (DGT) and on the discrete wavelet transform (DWT); a third feature extraction technique, based on the clustering of the wavelet coefficients, is also presented. The results carried out by artificial neural networks trained and tested using the described feature extraction techniques, demonstrate the usefulness of the clustered DWT method with respect to the well known techniques of DGT and DWT. [ABSTRACT FROM AUTHOR]

An automated algorithm is presented for the analysis and classification of eddy current bobbin probe data obtained from nuclear power plant steam generator tubes (SGT). This algorithm attempts to find a balance between the two seemingly conflicting requirements of SGT eddy current data analysis, namely, detecting and identifying as many of the actual defects as possible, whilst limiting the number of false alarms to a minimum. Initial results presented in this paper look very promising. [ABSTRACT FROM AUTHOR]

An algorithm is presented for the automated analysis of rotating probe multifrequency eddy current data obtained from nuclear power plant steam generator tubes (SGT). The algorithm consists of four steps, namely, a preprocessing stage for conditioning the data, a decision tree based feature extraction stage for identifying relevant features for analysis, a neural network based classification stage for identifying signals from various defect types and benign structures, and finally a blind deconvolution based characterization stage for accurately estimating the size and orientation of the detected defects. This algorithm is optimized to maximize the probability of detection (POD), while keeping the number of false alarms (PFA) at a minimum. Initial results presented in this paper look very promising and demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]